A drop-in MCP proxy that aggregates multiple backend servers into two meta-tools for efficient tool discovery and execution. It enables AI clients to access hundreds of tools while minimizing context window usage through searchable indexing.
Aggregates tools from multiple upstream MCP servers and exposes them through 4 meta-tools, enabling LLMs to discover and use hundreds of tools without loading all schemas upfront.
A proxy server that wraps existing MCP servers to significantly reduce token consumption by compressing tool descriptions into a two-step interface. It enables users to integrate extensive toolsets without exceeding context limits or incurring high API costs.
A single MCP endpoint for AI agents to browse, inspect, and call tools from multiple upstream MCP servers without loading all schemas upfront, reducing context overhead.
A context-aware MCP proxy that reduces token usage by exposing only 3 tools (mcp_search, mcp_call, mcp_schema) to LLMs, with on-demand tool loading and BM25 search.
An MCP aggregator that consolidates multiple MCP servers behind a single interface with just 3 tools (search, get details, execute), reducing context pollution for AI agents by avoiding direct exposure of numerous tool schemas.
